While enforcing invariance constraints on general classification models [1]}, [2]}, [3]} and collaborative filtering algorithms [4]} has received considerable attention in recent years, these techniques have yet to be considered within the context of graph embeddings—a setting that introduces particular challenges due to the non-i.i.d. and non-Euclidean nature of relational, graph data.
